Large-Scale Semantic Data Storage and Query Optimization Based on HBase

With the widespread application of semantic data, traditional RDF storage and query systems are facing bottlenecks in scalability and operational efficiency. How to efficiently organize and query large-scale semantic data has become an important issue for accurate processing of data elements and large-scale precision fine-tuning of artificial intelligence. To this end, this paper proposes an RDF data storage and query model based on the distributed database HBase. In terms of storage, in order to improve the high-concurrency and efficient query effect of HBase tables, a pre-partitioning storage method is proposed, which effectively solves the hot spots and load imbalance of data in HBase, and provides joint optimization support for subsequent parallel queries; in terms of query, combined with Bloom filters, skipping HFile retrieval optimizes the fine-grained retrieval service of data and improves query efficiency. Theoretical analysis and experimental results show that the storage and query optimization scheme proposed in this paper can achieve efficient storage and query of semantic big data.